QTPyLib, Pythonic Algorithmic Trading
QTPyLib, Pythonic Algorithmic Trading =====================================
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QTPyLib (Q\ uantitative T\ rading Py\ thon Lib\ rary) is a simple, event-driven algorithmic trading library written in Python, that supports backtesting, as well as paper and live trading via Interactive Brokers <https://www.interactivebrokers.com>_.
I developed QTPyLib because I wanted for a simple, yet powerful, trading library that will let me focus on the trading logic itself and ignore everything else.
Full Documentation » <http://www.qtpylib.io/>_
Changelog » <./CHANGELOG.rst>_
Read about the future of QTPyLib here: https://aroussi.com/post/the-future-of-qtpylib
Features ========
- A continuously-running Blotter that lets you capture market data even when your algos aren't running.
- Tick, Bar and Trade data is stored in MySQL for later analysis and backtesting.
- Using pub/sub architecture using
ØMQ <http://zeromq.org>_ (ZeroMQ) for communicating between the Algo and the Blotter allows for a single Blotter/multiple Algos running on the same machine. - Support for Order Book, Quote, Time, Tick or Volume based strategy resolutions.
- Includes many common indicators that you can seamlessly use in your algorithm.
- Market data events use asynchronous, non-blocking architecture.
- Have orders delivered to your mobile via SMS (requires a
Nexmo <https://www.nexmo.com/>orTwilio <https://www.twilio.com/>account). - Full integration with
TA-Lib <http://ta-lib.org>via dedicated module (see documentation <http://qtpylib.io/docs/latest/indicators.html#ta-lib-integration>). - Ability to import any Python library (such as
scikit-learn <http://scikit-learn.org>orTensorFlow <https://www.tensorflow.org>) to use them in your algorithms.
Quickstart ==========
There are 5 main components to QTPyLib:
- `
Blotter- handles market data retrieval and processing. Broker- sends and process orders/positions (abstracted layer).Algo- (sub-class ofBroker) communicates with theBlotterto pass market data to your strategies, and process/positions orders viaBroker.Reports- provides real-time monitoring of trades and open positions via Web App, as well as a simple REST API for trades, open positions, and market data.- Lastly, Your Strategies, which are sub-classes of
Algo, handle the trading logic/rules. This is where you'll write most of your code.
- Get Market Data
To get started, you need to first create a Blotter script:
.. code:: python
# blotter.py from qtpylib.blotter import Blotter
class MainBlotter(Blotter): pass # we just need the name
if name == "main": blotter = MainBlotter() blotter.run()
Then, with IB TWS/GW running, run the Blotter from the command line:
.. code:: bash
$ python blotter.py
If your strategy needs order book / market depth data, add the --orderbook flag to the command:
.. code:: bash
$ python blotter.py --orderbook
- Write your Algorithm
While the Blotter running in the background, write and execute your algorithm:
.. code:: python
# strategy.py from qtpylib.algo import Algo
class CrossOver(Algo):
def on_start(self): pass
def on_fill(self, instrument, order): pass
def on_quote(self, instrument): pass
def on_orderbook(self, instrument): pass
def on_tick(self, instrument): pass
def on_bar(self, instrument): # get instrument history bars = instrument.get_bars(window=100)
# or get all instruments history # bars = self.bars[-20:]
# skip first 20 days to get full windows if len(bars) < 20: return
# compute averages using internal rolling_mean bars['short_ma'] = bars['close'].rolling(window=10).mean() bars['long_ma'] = bars['close'].rolling(window=20).mean()
# get current position data positions = instrument.get_positions()
# trading logic - entry signal if bars['shortma'].crossedabove(bars['long_ma'])[-1]: if not instrument.pending_orders and positions["position"] == 0:
# buy one contract instrument.buy(1)
# record values for later analysis self.record(ma_cross=1)
# trading logic - exit signal elif bars['shortma'].crossedbelow(bars['long_ma'])[-1]: if positions["position"] != 0:
# exit / flatten position instrument.exit()
# record values for later analysis self.record(ma_cross=-1)
if name == "main": strategy = CrossOver( instruments = [ ("ES", "FUT", "GLOBEX", "USD", 201609, 0.0, "") ], # ib tuples resolution = "1T", # Pandas resolution (use "K" for tick bars) tick_window = 20, # no. of ticks to keep bar_window = 5, # no. of bars to keep preload = "1D", # preload 1 day history when starting timezone = "US/Central" # convert all ticks/bars to this timezone ) strategy.run()
To run your algo in a live enviroment, from the command line, type:
.. code:: bash
$ python strategy.py --logpath ~/qtpy/
The resulting trades be saved in ~/qtpy/STRATEGY_YYYYMMDD.csv for later analysis.
- Viewing Live Trades
While the Blotter running in the background, write the dashboard:
.. code:: python
# dashboard.py from qtpylib.reports import Reports
class Dashboard(Reports): pass # we just need the name
if name == "main": dashboard = Dashboard(port = 5000) dashboard.run()
To run your dashboard, run it from the command line:
.. code:: bash
$ python dashboard.py
>>> Dashboard password is: a0f36d95a9 >>> Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
Now, point your browser to http://localhost:5000 and use the password generated to access your dashboard.
.. note::
You can find other examples in the qtpylib/examples directory. Please refer to the Full Documentation _ to learn how to enable SMS notifications, use the bundled Indicators, and more.
Installation ============
Install using pip:
.. code:: bash
$ pip install qtpylib --upgrade --no-cache-dir
Requirements
- Python
_ >=3.4 - Pandas
_ (tested to work with >=0.18.1) - Numpy
_ (tested to work with >=1.11.1) - PyZMQ
_ (tested to work with >=15.2.1) - PyMySQL
_ (tested to work with >=0.7.6) - pytz
_ (tested to work with >=2016.6.1) - dateutil
_ (tested to work with >=2.5.1) - Nexmo-Python
_ for SMS support (tested to work with >=1.2.0) - Twilio-Python
_ for SMS support (tested to work with >=5.4.0) - Flask
_ for the Dashboard (tested to work with >=0.11) - Requests
_ (tested to work with >=2.10.0) - IbPy2
_ (tested to work with >=0.8.0) - ezIBpy
_ (IbPy wrapper, tested to work with >=1.12.66) - Latest Interactive Brokers’ TWS
orIB Gatewayinstalled and running on the machine - MySQL Server
Legal Stuff ===========
QTPyLib is licensed under the Apache License, Version 2.0. A copy of which is included in LICENSE.txt.
QTPyLib is not a product of Interactive Brokers, nor is it affiliated with Interactive Brokers.
P.S.
I'm very interested in your experience with QTPyLib. Please drop me a note with any feedback you have.
Ran
